Dr. Georgios Fellouris (USC)

Tittle: Decentralized sequential change detection

Abstract: Consider a number of dispersed sensors which transmit their observations to a decision maker at some central location. At some unknown time, an event causes an abrupt change in the dynamics of all sensors. The classical change detection problem is to detect the time of the change as soon as possible, while avoiding a large number of false alarms, using the sequentially acquired sensor observations.

However, in many applications, a limited communication bandwidth does not allow the implementation of the optimal centralized detection rule and calls for a decentralized formulation of the problem; that is, the decision maker has to detect the change under quantization constraints, as well as restrictions on the communication rate of the sensors.

In this talk, we will review existing approaches to the decentralized change detection problem. We will then propose a novel approach, which turns out to be efficient both from a statistical and a practical point of view. More specifically, the proposed detection rule has strong asymptotic optimality properties, under both a discrete-time and a continuous-time setup, while requiring a minimal communication activity in the sensor network. Finally, we will illustrate with simulation experiments that the proposed scheme is much more efficient than existing decentralized detection rules.